Patent Application for Machine Learning Model Identifying Foundational Items

ChangeBridge: Patent Apps - Business Methods (G06Q)

Detected March 27th, 2026

Summary

The USPTO has published a patent application (US20260087539A1) for a machine learning model designed to identify foundational items in online orders. The system aims to optimize order fulfillment by sequencing pickers to retrieve critical items earlier in the process.

View original document View source feed page

What changed

This document is a published patent application detailing an online system that uses a trained machine-learning model to predict and identify "foundational items" within user orders. These foundational items are defined as those whose unavailability might lead a user to cancel other items in the same order. The system's objective is to improve order fulfillment efficiency by presenting these foundational items to pickers earlier in the picking sequence, allowing for quicker identification of unavailability and enabling remedial actions before other items are collected.

While this is a patent application and not a regulation, it signifies potential future technological advancements in e-commerce and logistics. Companies operating in this space, particularly those involved in online retail and order fulfillment, should be aware of such innovations. The application highlights the growing use of AI and machine learning in optimizing operational processes, which could lead to new industry standards or best practices in the future. No immediate compliance actions are required, but monitoring patent filings in this area can provide insights into emerging trends.

Source document (simplified)

← USPTO Patent Applications

GENERATING AN ITEM SELECTION SEQUENCE USING A MACHINE LEARNING MODEL FOR IDENTIFYING FOUNDATIONAL ITEMS

Application US20260087539A1 Kind: A1 Mar 26, 2026

Inventors

Shaun Navin Maharaj, Mark Oberemk, Brent Scheibelhut, Madeline Mesard

Abstract

An online system receives orders from users and fulfills the orders by dispatching a picker to a physical source to obtain the items for delivery. Some items in an order may be considered “foundational,” meaning that a user who ordered the items may wish to cancel one or more other items in the order if the foundational item is unavailable (e.g., the item is a critical ingredient for a recipe). The online system predicts items in the order that are foundational using a trained machine-learning model. The online system presents the items to the picker in a sequence so the foundational items are obtained earlier by the picker. This enables the picker to observe whether the determined foundational item is available sooner in the picking process, allowing earlier performance of a remedial action and possibly avoiding replacing previously obtained items affected by the unavailability of the foundational item.